# Slice Tuner: A Selective Data Acquisition Framework for Accurate and Fair Machine Learning Models

arXiv: https://arxiv.org/abs/2003.04549 · HTML: https://mlsanity.com/paper/2003.04549/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2020.

## Built on by (most cited first, 13 of 13)

- [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021)
- [Mandoline: Model Evaluation under Distribution Shift](https://mlsanity.com/paper/2107.00643/) (2021)
- [Representation Bias in Data: A Survey on Identification and Resolution Techniques](https://mlsanity.com/paper/2203.11852/) (2022)
- [The Data Addition Dilemma](https://mlsanity.com/paper/2408.04154/) (2024)
- [Fairness Without Harm: An Influence-Guided Active Sampling Approach](https://mlsanity.com/paper/2402.12789/) (2024)
- [VLSlice: Interactive Vision-and-Language Slice Discovery](https://mlsanity.com/paper/2309.06703/) (2023)
- [Falcon: Fair Active Learning using Multi-armed Bandits](https://mlsanity.com/paper/2401.12722/) (2024)
- [Responsible AI Challenges in End-to-end Machine Learning](https://mlsanity.com/paper/2101.05967/) (2021)
- [Achieving Representative Data via Convex Hull Feasibility Sampling Algorithms](https://mlsanity.com/paper/2204.06664/) (2022)
- [Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play](https://mlsanity.com/paper/2302.05807/) (2023)
- [Non-Invasive Fairness in Learning through the Lens of Data Drift](https://mlsanity.com/paper/2303.17566/) (2023)
- [Unbiased Binning for Fairness-aware Attribute Representation](https://mlsanity.com/paper/2509.21785/) (2025)
- [Data Acquisition for Improving Model Fairness using Reinforcement Learning](https://mlsanity.com/paper/2412.03009/) (2024)

## Abstract (arXiv)

As machine learning becomes democratized in the era of Software 2.0, a serious bottleneck is acquiring enough data to ensure accurate and fair models. Recent techniques including crowdsourcing provide cost-effective ways to gather such data. However, simply acquiring data as much as possible is not necessarily an effective strategy for optimizing accuracy and fairness. For example, if an online app store has enough training data for certain slices of data (say American customers), but not for others, obtaining more American customer data will only bias the model training. Instead, we contend that one needs to selectively acquire data and propose Slice Tuner, which acquires possibly-different amounts of data per slice such that the model accuracy and fairness on all slices are optimized. This problem is different than labeling existing data (as in active learning or weak supervision) because the goal is obtaining the right amounts of new data. At its core, Slice Tuner maintains learning curves of slices that estimate the model accuracies given more data and uses convex optimization to find the best data acquisition strategy. The key challenges of estimating learning curves are that they may be inaccurate if there is not enough data, and there may be dependencies among slices where acquiring data for one slice influences the learning curves of others. We solve these issues by iteratively and efficiently updating the learning curves as more data is acquired. We evaluate Slice Tuner on real datasets using crowdsourcing for data acquisition and show that Slice Tuner significantly outperforms baselines in terms of model accuracy and fairness, even when the learning curves cannot be reliably estimated.